A look at Flames even strength play: scoring chances, shots and Goals

In every week’s black box we saw how well the Flames fared with each individual player on the ice, in terms of attempted shots, scoring chances, and actual goals (here’s the final weekly black box summary). This week we’ll take a look back and look at Calgary’s even-strength data in a variety of ways, separating offense from defense and seeing with which players the Flames enjoyed the most success converting attempted shots to scoring chances, and scoring chances to goals, and with which players they were most successful preventing opponents from doing the same.

Who Drives the offense?

Who drives Calgary’s offense at even-strength? Normally we looked at what percentage of all on-ice attempted shots, scoring chances and goals were enjoyed by the Calgary Flames instead of their opponents, but in this year-end feature we’ll instead look at with which players Calgary was most successful converting those attempted shots into chances, and chances into goals.

Generally 3 out of every 10 attempted shots (30%) results in a scoring chance, and about half as many into actual goals (15%). Gifted playmakers, or those who play alongside them, can understandably cause a higher percentage of shots to convert into chances, or chances into goals.

The Forwards

Here’s a look at all of Calgary’s forwards (minimum 5 games played) with the number of scoring chances created by every attempted shot, followed by the number of goals created by every such scoring chance, and sorted by the final column, the number of goals created by every attempted shot. The list is headlined by Sven Baertschi, with whom 12% of all attempted shots result in goals, over double the rate of anyone else.

The bar is set by gifted playmaker Alex Tanguay, which whom Calgary converted 37% of all attempted shots into scoring chances. Behind him you had talented stars like Mike Cammalleri (35%) and Jarome Iginla (33%) – and in limited ice-time Sven Beartschi enjoyed a sky-high 38% conversion rate. Of course, keep in mind the youngster only appeared in three NHL games, so there are sample size issues with his results.

While most Flames were within a percentage point or two of the more typical rate of 30%, some skaters saw an alarmingly low percentage of on-ice shots result in chances – most likely because they played with relatively unskilled offensive players. This list includes Guillaume Desbiens (20%), Tim Jackman (22%), and Tom Kostopoulos (24%) but also Krys Kolanos (21%) and David Moss (23%).

Converting scoring chances to goals involves being a gifted sniper, playing alongside one, or getting lucky. Obviously Calgary’s leaders are Olli Jokinen and Curtis Glencross at 19%, who both enjoyed tremendously good fortune in capitalizing on their opportunities (luck does appear to play a bigger role here given Glencross’ incredible personal shooting percentage).

Once again Sven Baertschi topped them all in his short stint, converting chances to goals at a tremendous 30% rate (Greg Nemisz not far behind at 25%). While most Flames were within a couple percentage points of the typical 15%, some of the same players who had difficulty converting shots into chances also struggled at converting chances into goals, like Guillaume Desbiens (0%), Lance Bouma and Blair Jones (6%), Tim Jackman (7%), Krys Kolanos (9%) and Blake Comeau and Mikael Backlund (10%).

The Defensemen

Now let’s look at the defensemen, who understandably have a more limited ability to help convert opportunities, and are therefore contained in a tighter pack.

While you wouldn’t expect most defensemen to have much of an ability to influence the conversion of shots into chances and chances into goals (unless your name is Erik Karlsson), it’s still surprising to see Scott Hannan atop the pack above Mark Giordano and T.J. Brodie, and to see Jay Bouwmeester tucked between Derek Smith and Cory Sarich at the bottom. That’s probably more randomness than anything else, however.

Who drives the defense?

Let’s look at the other side of the ice, and who drives the defense. While defensemen wouldn’t be expected to have as much influence in converting Calgary’s shots and chances into goals, they ought to have the most say in preventing opponents from doing the same. Unfortunately it appears that the results aren’t in order of the best defensive players, but rather those who faced the weakest opponents: Clay Wilson and Anton Babchuk.

Calgary’s top-four of Bouwmeester, Butler, Hannan and Giordano were all within a couple percentage points of the averages of 30% and 15%, with Scott Hannan a little bit worse than the others.

Those that played primarily against depth lines, like Brodie, Smith, and Sarich, all had greater success in preventing the conversion of shots into chances, but not necessarily chances into goals – Derek Smith was worst on the team, in fact. Perhaps quality of competition isn’t as great a factor there.

There doesn’t appear to be a great deal to learn here, at least among defensemen. And checking the forwards it appears that they’re once again ranked in order of who faces the weakest opposition.

Quality of teammates and competition seem to have a big impact on the ability to convert or prevent the conversion of shots into chances and chances into goals.

Most of the forwards are within a few percentage points of 30% when it comes to preventing the conversion of attempted shots into scoring chances, with those facing weak competition doing far better, like Guillaume Desbiens, Krys Kolanos, Paul Byron, Roman Horak, Lance Bouma and Tom Kostopoulos faring quite well and only Mike Cammalleri really faring far worse – although it’s interesting that Blair Jones didn’t do any better than the top-liners.

There is however one surprise, which is the prevention of scoring chances into goals. Despite playing against depth opponents only, the Flames that fared worse were Tim Jackman and Tom Kostopoulos. Something clearly went very wrong with those two this year.

Wrapping Up

Every week we looked at which players were best at generating favourable puck possession conditions for the Calgary Flames with relatively little emphasis at who was helping convert those conditions into chances and goals.

While we see that there was little to learn defensively, since the ability to prevent conversion of shots into chances and goals was basically a direct result of the quality of one’s competition (with the exception of a few curious cases), we did find a few players who were able to have success offensively.

Clearly some players have the playmaking gift to convert attempted shots into scoring chances (Tanguay), and a few more can convert those chances into goals (Jokinen, Glencross). Perhaps Alex Tanguay should have played with Jokinen and Glencross? Coming up next week we’ll take our last deep statistical look at Calgary’s special teams, before wrapping it up with a quick look at their three goaltenders.

9 Comments |

Really interesting stuff. Just a couple follow up questions and thoughts.

-Do we have any idea where league average SA to SC and SC to G conversion rates fall? You say generally there is a 30% SA to SC and 15% SC to G conversion rate, were those the league average numbers, team average numbers?

-the lament for sussing out defensive contributions being crowded out by quality of competition is well taken. I always thought that if someone could scrape the data, differential rates would help highlight some of these problems. For example, a calculation showing the average corsi rate differential amongst all opponents while a player was on the ice. Super complex calculation and data management, but it would control for a lot, but not all, quality of competition issues.

-do you have the data for similar rates for our goaltenders – was there any differential from Kiprusoff to his back ups? Are there any goalies that exhibit a differential in the league? It would seem such data should be useful/detrimental to the “but Kiprusoff makes huge saves!” camp that tries to justify his cap hit.

I also apologize that this piece isn’t exactly my best work. I’ll happily field any questions people have on Calgary’s even-strength play here in the Comments section (and I’m sure Kent will contribute too).

To answer the outstanding questions from Tach:

1. Kent is right, the 30% and 15% is typical, and variations might be more a consequence of how the scoring chances are being recorded than in actual differences in play.

2. I prefer percentages over differential. Players A and B could both be +10, but Player A is 15 for and 5 against, and Player B is 35 for and 25 against – big difference! Player A is 75% and Player B is 58.3%.

And even if we did what you said we’d still have problems. First line players tend to match up against first line players, so you’d just wind up with the average corsi percentage of one’s fellow first-line players.

3. Do you mean the difference in save percentages between starting goalies and the other goalies? Yes I do that from time to time, and yes there’s always been a HUGE different between Miikka Kiprusoff and Calgary’s other goalies. There’s also been a HUGE difference between a goalie’s save percentage as a Flame, and as a non-Flame. Certain teams really kill a goalie’s save percentage, like Toronto and Tampa Bay, and Calgary’s on the ass end of that list.

On the goalies, I know the EVSV% for Kipper is much higher than his back-ups, but I meant whether his save/scoring chance percentage was similarly high. Whenever I bring up Kipper’s occassional substandard performance over the last few years to someone less sophisticated than the average Flames Nation commenter, inevitably the first response I get is “The defence sucked and gave him no help”. It would seem to me that if his save percentage on scoring chances lagged behind league average as far as his save percentage on shots, it would be fairly strong evidence this particular brand of “seen him good” was not reflective of the data.

On point 2, I like the percentages as well for the most part because I totally agree rates can be misleading.

I still think that calculating differential, if we could plough through the data, would be useful. I agree that seeing “first line player vs. first line players” would cloud some of the effects. But wouldn’t it also give us “When Bouwmeester played against first line players, their average corsi percentage dropped by .5%, when he played third line players their corsi percentage dropped by 7%. When Cory Sarich played first line players their corsi percentage increased 6%, and third liners had theirs increase 2%” (all numbers hypothetical and made up of course). I think that would be interesting data if someone with the skills to scrape it from timeoneice/behind the net could make it work. I profess I would do it but for a lack of time and know how.